Customer Segmentation with PCA and Clustering¶

Project Introduction¶

In this project, I will embark on a journey to gain deeper insights into a grocery firm's customer database. The primary objective is customer segmentation, a crucial practice that involves categorizing customers into distinct groups based on shared characteristics. By doing so, we aim to enhance our understanding of customer behavior and tailor our products and services to meet their diverse needs and preferences.

Understanding the Dataset¶

Link to dataset:¶

https://www.kaggle.com/datasets/imakash3011/customer-personality-analysis

Dataset Description¶

There are four groups of information:

People
ID Year_Birth Education Marital_Status Income Kidhome Teenhome Dt_Customer Recency Complain
Customer's unique identifier Customer's birth year Customer's education level Customer's marital status Customer's yearly household income Number of children in customer's household Number of teenagers in customer's household Date of customer's enrollment with the company Number of days since customer's last purchase 1 if the customer complained in the last 2 years, 0 otherwise
Products
MntWines MntFruits MntMeatProducts MntFishProducts MntSweetProducts MntGoldProds
Amount spent on wine in the last 2 years Amount spent on fruits in the last 2 years Amount spent on meat in the last 2 years Amount spent on fish in the last 2 years Amount spent on sweets in the last 2 years Amount spent on gold in the last 2 years
Promotion
NumDealsPurchases AcceptedCmp1 AcceptedCmp2 AcceptedCmp3 AcceptedCmp4 AcceptedCmp5 Response
Number of purchases made with a discount 1 if the customer accepted the offer in the 1st campaign, 0 otherwise 1 if the customer accepted the offer in the 2nd campaign, 0 otherwise 1 if the customer accepted the offer in the 3rd campaign, 0 otherwise 1 if the customer accepted the offer in the 4th campaign, 0 otherwise 1 if the customer accepted the offer in the 5th campaign, 0 otherwise 1 if the customer accepted the offer in the last campaign, 0 otherwise
Place
NumWebPurchases NumCatalogPurchases NumStorePurchases NumWebVisitsMonth
Number of purchases made through the company’s website Number of purchases made using a catalogue Number of purchases made directly in stores Number of visits to the company’s website in the last month

Table of Contents¶

  1. Import Libraries and Get to know Dataset

    • 1.1. Import necessary Libraries and Packages
    • 1.2. Get basic information about the dataset
    • 1.3. Examine unique values of each column

  2. Data Visualization and Transformation

    • 2.1. Visualize and transform categorical features
    • 2.2. Visualize and transform numerical features
    • 2.3. Drop redundant variables
    • 2.4. Correlation Analysis

  3. Preprocessing

    • 3.1. Encoding
    • 3.2. Scale the Data

  4. Dimensionality Reduction

    • 4.1. Initialize PCA object
    • 4.2. Identify the Optimal Number of Components
    • 4.3. Conclusion about n_components

  5. Customer Segmentation using K-Means Clustering

    • 5.1. Initialize model
    • 5.2. Choose k
    • 5.3. Determining the Optimal Number of Clusters: Elbow vs. Silhouette

  6. Evaluate the Clusters

  7. Final conclusion

1. Import Libraries and Get to know Dataset¶

1.1. Import necessary Libraries and Packages¶

In [1]:
import pandas as pd
import numpy as np
pd.set_option('display.float_format', '{:.2f}'.format)
pd.set_option('display.max_columns', None)

import matplotlib.pyplot as plt
import plotly.express as px
import seaborn as sns

from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
from sklearn.cluster import KMeans
from sklearn.metrics import silhouette_score
from yellowbrick.cluster import KElbowVisualizer, SilhouetteVisualizer


sns.set_theme(style='white')
%config InlineBackend.figure_format = 'retina'
plt.rcParams["axes.spines.right"] = False
plt.rcParams["axes.spines.top"] = False

1.2. Get basic information about the dataset¶

In [2]:
original_df = pd.read_csv('marketing_campaign.csv', delimiter='\t')
print(original_df.shape)
original_df.head()

(2240, 29)
Out[2]:
ID Year_Birth Education Marital_Status Income Kidhome Teenhome Dt_Customer Recency MntWines MntFruits MntMeatProducts MntFishProducts MntSweetProducts MntGoldProds NumDealsPurchases NumWebPurchases NumCatalogPurchases NumStorePurchases NumWebVisitsMonth AcceptedCmp3 AcceptedCmp4 AcceptedCmp5 AcceptedCmp1 AcceptedCmp2 Complain Z_CostContact Z_Revenue Response
0 5524 1957 Graduation Single 58138.00 0 0 04-09-2012 58 635 88 546 172 88 88 3 8 10 4 7 0 0 0 0 0 0 3 11 1
1 2174 1954 Graduation Single 46344.00 1 1 08-03-2014 38 11 1 6 2 1 6 2 1 1 2 5 0 0 0 0 0 0 3 11 0
2 4141 1965 Graduation Together 71613.00 0 0 21-08-2013 26 426 49 127 111 21 42 1 8 2 10 4 0 0 0 0 0 0 3 11 0
3 6182 1984 Graduation Together 26646.00 1 0 10-02-2014 26 11 4 20 10 3 5 2 2 0 4 6 0 0 0 0 0 0 3 11 0
4 5324 1981 PhD Married 58293.00 1 0 19-01-2014 94 173 43 118 46 27 15 5 5 3 6 5 0 0 0 0 0 0 3 11 0
In [3]:
df = original_df.copy()
In [4]:
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2240 entries, 0 to 2239
Data columns (total 29 columns):
 #   Column               Non-Null Count  Dtype  
---  ------               --------------  -----  
 0   ID                   2240 non-null   int64  
 1   Year_Birth           2240 non-null   int64  
 2   Education            2240 non-null   object 
 3   Marital_Status       2240 non-null   object 
 4   Income               2216 non-null   float64
 5   Kidhome              2240 non-null   int64  
 6   Teenhome             2240 non-null   int64  
 7   Dt_Customer          2240 non-null   object 
 8   Recency              2240 non-null   int64  
 9   MntWines             2240 non-null   int64  
 10  MntFruits            2240 non-null   int64  
 11  MntMeatProducts      2240 non-null   int64  
 12  MntFishProducts      2240 non-null   int64  
 13  MntSweetProducts     2240 non-null   int64  
 14  MntGoldProds         2240 non-null   int64  
 15  NumDealsPurchases    2240 non-null   int64  
 16  NumWebPurchases      2240 non-null   int64  
 17  NumCatalogPurchases  2240 non-null   int64  
 18  NumStorePurchases    2240 non-null   int64  
 19  NumWebVisitsMonth    2240 non-null   int64  
 20  AcceptedCmp3         2240 non-null   int64  
 21  AcceptedCmp4         2240 non-null   int64  
 22  AcceptedCmp5         2240 non-null   int64  
 23  AcceptedCmp1         2240 non-null   int64  
 24  AcceptedCmp2         2240 non-null   int64  
 25  Complain             2240 non-null   int64  
 26  Z_CostContact        2240 non-null   int64  
 27  Z_Revenue            2240 non-null   int64  
 28  Response             2240 non-null   int64  
dtypes: float64(1), int64(25), object(3)
memory usage: 507.6+ KB

Change Year_Enrolled to datetime¶

In [5]:
df['Year_Enrolled'] = pd.to_datetime(df['Dt_Customer'], format='%d-%m-%Y')
In [6]:
missing_encountered = False

for col in df.columns:
    missing = df[col].isnull().sum()
    if missing > 0:
        print(f'{col}: {missing} missing values.')
        missing_encountered = True
        
if not missing_encountered:
    print("No missing values.")

Income: 24 missing values.

The dataset comprises 29 columns, out of which 25 are integer variables, 3 are objects, and 1 is a float. And Income variable is the only one having missing values.

1.3. Examine unique values of each column¶

In [7]:
data = []
for col in df.columns:
    unique_values = df[col].unique()
    num_unique = len(unique_values)
    dtype = df[col].dtype
    if num_unique > 10:
        data.append((col, num_unique, "", dtype))
    else:
        unique_values_str = ", ".join(map(str, unique_values))
        data.append((col, num_unique, unique_values_str, dtype))

uniques = pd.DataFrame(data, columns=["Column", "# of Uniques", "Unique Values (<10)", "Dtype"])
uniques.sort_values(by='# of Uniques').reset_index(drop=True)
Out[7]:
Column # of Uniques Unique Values (<10) Dtype
0 Z_Revenue 1 11 int64
1 Z_CostContact 1 3 int64
2 AcceptedCmp2 2 0, 1 int64
3 Response 2 1, 0 int64
4 AcceptedCmp1 2 0, 1 int64
5 AcceptedCmp5 2 0, 1 int64
6 AcceptedCmp3 2 0, 1 int64
7 Complain 2 0, 1 int64
8 AcceptedCmp4 2 0, 1 int64
9 Teenhome 3 0, 1, 2 int64
10 Kidhome 3 0, 1, 2 int64
11 Education 5 Graduation, PhD, Master, Basic, 2n Cycle object
12 Marital_Status 8 Single, Together, Married, Divorced, Widow, Al... object
13 NumCatalogPurchases 14 int64
14 NumStorePurchases 14 int64
15 NumDealsPurchases 15 int64
16 NumWebPurchases 15 int64
17 NumWebVisitsMonth 16 int64
18 Year_Birth 59 int64
19 Recency 100 int64
20 MntFruits 158 int64
21 MntSweetProducts 177 int64
22 MntFishProducts 182 int64
23 MntGoldProds 213 int64
24 MntMeatProducts 558 int64
25 Year_Enrolled 663 datetime64[ns]
26 Dt_Customer 663 object
27 MntWines 776 int64
28 Income 1975 float64
29 ID 2240 int64

Note:

  • 1 Unique Value: Variables Z_Revenue and Z_CostContact exhibit a singular value, implying no contribution to our clustering analysis. Consequently, I will remove them from the dataset.

  • 2 Unique Values: Variables AcceptedCmp1 to AcceptedCmp5, Complain, and Response adopt binary options. I intend to visualize them to assess the potential for combining these variables.

  • 3 Unique Values: Variables Teenhome and Kidhome can be amalgamated into a single variable named Dependents.

  • Education: The Education variable entails five categories: 'Graduation', 'PhD', 'Master', 'Basic', and '2n Cycle'. After researching the significance of 'Basic' and '2n Cycle' education levels:

    • 'Basic' signifies foundational or primary education. This category will be retained as is.
    • '2n Cycle' represents advanced studies beyond a bachelor's degree (1st Cycle). It will be merged with the 'Master' category.

  • Marital_Status: With eight categories, namely Single, Together, Married, Divorced, Widow, Absurb, YOLO, I propose merging them into two categories: 'Relationship' and 'Single'.

  • ID: This variable is deemed non-contributory and will be excluded from further analysis.

  • Year_Birth: To enhance interpretability, I will transform the Year_Birth variable into Age using the maximum engagement day as a reference threshold.

  • Year_Enrolled: Similar to Year_Birth, I will create a new variable named Days_Enrolled by employing the same methodology.

  • Other categorical variables that encompass more than 10 options will remain unaltered.

2. Data Visualization and Transformation¶

In this stage, I will conduct data cleaning as mentioned above:

Before that, I will plot some chart:

2.1. Visualize and transform categorical features¶

In [8]:
categorical_cols = [
    'Kidhome', 
    'Teenhome', 
    'AcceptedCmp1', 
    'AcceptedCmp2', 
    'AcceptedCmp3', 
    'AcceptedCmp4', 
    'AcceptedCmp5',
    'Complain', 
    'Response',
    'Education',
    'Marital_Status'
]

fig, axes = plt.subplots(3, 4, figsize=(16, 12))

for i, col in enumerate(categorical_cols):
    order = df[col].value_counts().index
    ax = axes[i // 4, i % 4]
    sns.countplot(data=df, x=col, ax=ax, order=order, palette='magma')
    ax.set_title(col)
    ax.set_xlabel('')
    ax.set_ylabel('')
    
    if col == 'Marital_Status':  
        ax.tick_params(axis='x', rotation=45)

plt.tight_layout()

Examine Complain¶

In [9]:
df['Complain'].value_counts()
Out[9]:
0    2219
1      21
Name: Complain, dtype: int64

The highly imbalanced distribution of the Complain variable (21 occurrences of 1 and 2219 occurrences of 0) might not contribute meaningful insights to customer segmentation so I will drop this variable.

Examine AcceptedCmp1, AcceptedCmp2, AcceptedCmp3, AcceptedCmp4 and AcceptedCmp5:¶

In [10]:
accepted = df[['AcceptedCmp1', 'AcceptedCmp2', 'AcceptedCmp3', 'AcceptedCmp4', 'AcceptedCmp5']]
plt.figure(figsize=(8, 6))
sns.heatmap(accepted, cbar=False)
plt.title('Accepted Campaigns');

I think we can create a Accepted_Campaign variable to sum up the fives:

In [11]:
df['Accepted_Campaign'] = df[['AcceptedCmp3', 'AcceptedCmp4', 'AcceptedCmp5', 'AcceptedCmp1', 'AcceptedCmp2']].sum(axis=1)
prop_accepted_campaign = df['Accepted_Campaign'].value_counts(normalize=True)

sns.countplot(x=df['Accepted_Campaign'], palette='magma')
for i, prop in enumerate(prop_accepted_campaign):
    plt.text(i, prop * len(df), f'{prop:.2%}', ha='center', va='bottom', color='black', fontsize=12)
plt.title('Distribution of Total Accepted Campaigns');

The distribution of accepted campaigns among customers is skewed, with most customers (approximately 79%) not accepting any campaigns. Around 15% of customers accepted one campaign, while smaller proportions engaged with two, three, or four campaigns, with the latter category having negligible representation.

Create Dependents variable¶

In [12]:
df['Dependents'] = df['Kidhome'] + df['Teenhome']

Grouping Marital_Status¶

In [13]:
group_mapping = {
    'Married': 'Relationship',
    'Together': 'Relationship',
    'Single': 'Single',
    'Divorced': 'Single',
    'Widow': 'Single',
    'Alone': 'Single',
    'Absurd': 'Single',
    'YOLO': 'Single'
}

df['Marital_Status'] = df['Marital_Status'].map(group_mapping)

Grouping Education¶

In [14]:
education_mapping = {
    'Graduation': 'Graduation',
    'PhD': 'PhD',
    'Master': 'Master',
    'Basic': 'Basic',
    '2n Cycle': 'Master'  
}

df['Education'] = df['Education'].map(education_mapping)
In [15]:
plt.figure(figsize=(12, 3.5))

categories = ['Dependents', 'Marital_Status', 'Education']
palette = 'magma'

for i, col in enumerate(categories, 1):
    plt.subplot(1, 3, i)
    order = df[col].value_counts().index
    sns.countplot(data=df, x=col, palette=palette, order=order)
    plt.title(f'Count of {col}')
    plt.ylabel('')

plt.tight_layout();

2.2. Visualize and transform numerical features¶

In [16]:
numeric_cols = [
    'Income', 'Recency', 
    'MntWines', 'MntFruits', 'MntMeatProducts', 'MntFishProducts', 'MntSweetProducts', 'MntGoldProds', 
    'NumDealsPurchases', 
    'NumWebPurchases', 'NumCatalogPurchases', 'NumStorePurchases', 
    'NumWebVisitsMonth'
]

fig, axes = plt.subplots(5, 3, figsize=(15, 20))

for i, col in enumerate(numeric_cols):
    ax = axes[i // 3, i % 3]
    sns.histplot(data=df, x=col, ax=axes[i // 3, i % 3])
    ax.set_xlabel('')
    ax.set_ylabel('')
    ax.set_title(f'Histogram of {col}')
plt.tight_layout();

Examine outliers of Income¶

In [17]:
median_income = df['Income'].median()
df['Income'] = df['Income'].fillna(median_income)
In [18]:
df[df['Income'] > 100000]
Out[18]:
ID Year_Birth Education Marital_Status Income Kidhome Teenhome Dt_Customer Recency MntWines MntFruits MntMeatProducts MntFishProducts MntSweetProducts MntGoldProds NumDealsPurchases NumWebPurchases NumCatalogPurchases NumStorePurchases NumWebVisitsMonth AcceptedCmp3 AcceptedCmp4 AcceptedCmp5 AcceptedCmp1 AcceptedCmp2 Complain Z_CostContact Z_Revenue Response Year_Enrolled Accepted_Campaign Dependents
124 7215 1983 Graduation Single 101970.00 0 0 12-03-2013 69 722 27 102 44 72 168 0 6 8 13 2 0 1 1 1 0 0 3 11 1 2013-03-12 3 0
164 8475 1973 PhD Relationship 157243.00 0 1 01-03-2014 98 20 2 1582 1 2 1 15 0 22 0 0 0 0 0 0 0 0 3 11 0 2014-03-01 0 1
203 2798 1977 PhD Relationship 102160.00 0 0 02-11-2012 54 763 29 138 76 176 58 0 7 9 10 4 0 1 1 1 0 0 3 11 1 2012-11-02 3 0
252 10089 1974 Graduation Single 102692.00 0 0 05-04-2013 5 168 148 444 32 172 148 1 6 9 13 2 0 1 1 1 1 0 3 11 1 2013-04-05 4 0
617 1503 1976 PhD Relationship 162397.00 1 1 03-06-2013 31 85 1 16 2 1 2 0 0 0 1 1 0 0 0 0 0 0 3 11 0 2013-06-03 0 2
646 4611 1970 Graduation Relationship 105471.00 0 0 21-01-2013 36 1009 181 104 202 21 207 0 9 8 13 3 0 0 1 1 0 0 3 11 1 2013-01-21 2 0
655 5555 1975 Graduation Single 153924.00 0 0 07-02-2014 81 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 3 11 0 2014-02-07 0 0
687 1501 1982 PhD Relationship 160803.00 0 0 04-08-2012 21 55 16 1622 17 3 4 15 0 28 1 0 0 0 0 0 0 0 3 11 0 2012-08-04 0 0
1300 5336 1971 Master Relationship 157733.00 1 0 04-06-2013 37 39 1 9 2 0 8 0 1 0 1 1 0 0 0 0 0 0 3 11 0 2013-06-04 0 1
1653 4931 1977 Graduation Relationship 157146.00 0 0 29-04-2013 13 1 0 1725 2 1 1 0 0 28 0 1 0 0 0 0 0 0 3 11 0 2013-04-29 0 0
1898 4619 1945 PhD Single 113734.00 0 0 28-05-2014 9 6 2 3 1 262 3 0 27 0 0 1 0 0 0 0 0 0 3 11 0 2014-05-28 0 0
2132 11181 1949 PhD Relationship 156924.00 0 0 29-08-2013 85 2 1 2 1 1 1 0 0 0 0 0 0 0 0 0 0 0 3 11 0 2013-08-29 0 0
2233 9432 1977 Graduation Relationship 666666.00 1 0 02-06-2013 23 9 14 18 8 1 12 4 3 1 3 6 0 0 0 0 0 0 3 11 0 2013-06-02 0 1

Note:

There are 13 individuals with an income exceeding 100,000, all of whom possess education starting from 'Graduation' level and beyond. The highest reported income among them stands at 666,666.

Now let's re-plot:

In [19]:
plt.figure(figsize=(5, 3.5))

sns.histplot(data=df[~(df['Income'] > 100000)], x='Income')
plt.axvline(df['Income'].mean(), color='red', linestyle='--', label=f"Mean of {df['Income'].mean():.0f}")
plt.title('Income histogram when removing outliers')
plt.legend();

Note:

The distribution now is pretty normal.

Create Day_Enrolled¶

In [20]:
threshold = df['Year_Enrolled'].max()

df['Day_Enrolled'] = (threshold - df['Year_Enrolled']).dt.days

Create Age¶

In [21]:
threshold = df['Year_Enrolled'].dt.year.max()

df['Age'] = threshold - df['Year_Birth']
In [22]:
plt.figure(figsize=(10, 3.5))

plt.subplot(1, 2, 1)
sns.histplot(data=df, x='Age')
plt.title('Age histogram');
plt.subplot(1, 2, 2)
sns.histplot(data=df, x='Day_Enrolled')
plt.title('Day_Enrolled histogram')
plt.ylabel('');

Check Age's outliers¶

In [23]:
df[df['Age'] > 80]
Out[23]:
ID Year_Birth Education Marital_Status Income Kidhome Teenhome Dt_Customer Recency MntWines MntFruits MntMeatProducts MntFishProducts MntSweetProducts MntGoldProds NumDealsPurchases NumWebPurchases NumCatalogPurchases NumStorePurchases NumWebVisitsMonth AcceptedCmp3 AcceptedCmp4 AcceptedCmp5 AcceptedCmp1 AcceptedCmp2 Complain Z_CostContact Z_Revenue Response Year_Enrolled Accepted_Campaign Dependents Day_Enrolled Age
192 7829 1900 Master Single 36640.00 1 0 26-09-2013 99 15 6 8 7 4 25 1 2 1 2 5 0 0 0 0 0 1 3 11 0 2013-09-26 0 1 276 114
239 11004 1893 Master Single 60182.00 0 1 17-05-2014 23 8 0 5 7 0 2 1 1 0 2 4 0 0 0 0 0 0 3 11 0 2014-05-17 0 1 43 121
339 1150 1899 PhD Relationship 83532.00 0 0 26-09-2013 36 755 144 562 104 64 224 1 4 6 4 1 0 0 1 0 0 0 3 11 0 2013-09-26 1 0 276 115

Note:

Only 3 people more than 80 years old but more interestingly, they are all above 110 years old, with the oldest is 121 years.

In [24]:
plt.figure(figsize=(5, 3.5))
sns.histplot(data=df[~(df['Age'] > 80)], x='Age')
plt.axvline(df['Age'].mean(), color='red', linestyle='--', label= f"Mean of {df['Age'].mean():.0f}")
plt.title('Age histogram when removing outliers')
plt.legend();

Note:

The age distribution exhibits a normal pattern after the removal of outliers.

Create Total_Cost and Total_Purchase:¶

In [25]:
df['Total_Cost'] = df['MntWines'] + df['MntFruits'] + df['MntMeatProducts'] + df['MntFishProducts'] + df['MntSweetProducts'] + df['MntGoldProds']

df['Total_Purchases'] = df['NumWebPurchases'] + df['NumCatalogPurchases'] + df['NumStorePurchases']
In [26]:
plt.figure(figsize=(10, 3.5))

plt.subplot(1, 2, 1)
sns.histplot(data=df, x='Total_Cost', bins=range(0,2501,100))
plt.title('Total_Cost histogram');
plt.subplot(1, 2, 2)
sns.histplot(data=df, x='Total_Purchases', bins=range(0,35,1))
plt.title('Total_Purchases histogram')
plt.ylabel('');

2.3. Drop redundant variables¶

Droping is important because it can helps to reduce the number of dimensionsity and complexity of model

In [27]:
corr = df.corr(numeric_only=True)
mask = np.triu(np.ones(corr.shape), k=1).astype(np.bool_)
corr = corr.where(mask)
corr = corr.unstack().sort_values(ascending=False).to_frame(name='Correlation')
corr = corr[corr['Correlation'] != 1].reset_index()
corr = corr.dropna()
corr.head(20)
Out[27]:
level_0 level_1 Correlation
0 Total_Cost MntWines 0.89
1 Total_Purchases NumStorePurchases 0.86
2 Total_Cost MntMeatProducts 0.84
3 Total_Purchases Total_Cost 0.82
4 Total_Purchases NumCatalogPurchases 0.79
5 Total_Cost NumCatalogPurchases 0.78
6 Total_Purchases NumWebPurchases 0.77
7 Total_Purchases MntWines 0.76
8 NumCatalogPurchases MntMeatProducts 0.72
9 Accepted_Campaign AcceptedCmp5 0.72
10 Dependents Teenhome 0.70
11 Dependents Kidhome 0.69
12 Accepted_Campaign AcceptedCmp1 0.68
13 Total_Cost NumStorePurchases 0.67
14 Total_Cost Income 0.66
15 Total_Cost MntFishProducts 0.64
16 NumStorePurchases MntWines 0.64
17 NumCatalogPurchases MntWines 0.64
18 Total_Purchases MntMeatProducts 0.62
19 Total_Purchases Income 0.62

Note:

It's evident that the newly-created variables are inherently correlated with the variables from which they are derived. Therefore, I've decided to retain only the total variables in order to mitigate dimensionality and simplify the model's complexity.

In [28]:
cleaned_df = df.copy().drop(
    columns=
    [
        'Year_Birth', 
        'ID', 
        'Z_CostContact', 'Z_Revenue', 
        'Dt_Customer', 
        'Year_Enrolled',
        'Complain',
        'MntWines', 'MntFruits', 'MntMeatProducts', 'MntFishProducts', 'MntSweetProducts', 'MntGoldProds', # Total Cost
        'NumWebPurchases', 'NumCatalogPurchases', 'NumStorePurchases', # Total Purchase
        'AcceptedCmp1', 'AcceptedCmp2', 'AcceptedCmp3', 'AcceptedCmp4', 'AcceptedCmp5', # Accepted_Campaign
        'Kidhome', 'Teenhome' # Total Dependents
    ]
)

print(cleaned_df.shape)
cleaned_df.head()

(2240, 13)
Out[28]:
Education Marital_Status Income Recency NumDealsPurchases NumWebVisitsMonth Response Accepted_Campaign Dependents Day_Enrolled Age Total_Cost Total_Purchases
0 Graduation Single 58138.00 58 3 7 1 0 0 663 57 1617 22
1 Graduation Single 46344.00 38 2 5 0 0 2 113 60 27 4
2 Graduation Relationship 71613.00 26 1 4 0 0 0 312 49 776 20
3 Graduation Relationship 26646.00 26 2 6 0 0 1 139 30 53 6
4 PhD Relationship 58293.00 94 5 5 0 0 1 161 33 422 14

2.4. Correlation Analysis¶

In [29]:
corr = cleaned_df.corr(numeric_only=True)

plt.figure(figsize=(7,7))
sns.heatmap(corr, 
            mask=np.triu(corr), 
            annot=True, 
            cmap = sns.diverging_palette(145, 300, s=60, as_cmap=True), 
            linecolor='w', 
            linewidth=0.1, 
            fmt='.2f', 
            annot_kws={'size':9});

Note:

  1. Income and Total_Cost: The correlation between 'Income' and 'Total_Cost' is notably strong (0.66). This suggests that customers with higher incomes tend to have higher total costs.

  2. NumWebVisitsMonth and Total_Purchases: There is a relatively strong negative correlation between 'NumWebVisitsMonth' and 'Total_Purchases' (-0.43). This implies that as the number of web visits per month decreases, the total number of purchases tends to increase.

  3. Accepted_Campaign and Response: The variables 'Accepted_Campaign' and 'Response' have a positive correlation of 0.43. This suggests that customers who responded positively to campaigns are more likely to have accepted those campaigns.

  4. Total_Cost and Total_Purchases: 'Total_Cost' and 'Total_Purchases' are highly positively correlated (0.82). This indicates that higher total costs correspond to a higher number of total purchases.

  5. Age and Total_Purchases: 'Age' and 'Total_Purchases' have a positive correlation of 0.16. This indicates that, in general, older customers tend to have more total purchases.

  6. Dependents and NumDealsPurchases: 'Dependents' and 'NumDealsPurchases' have a relatively strong positive correlation (0.44). This suggests that customers with more dependents tend to make more deal purchases.

  7. Recency and Response: There is a negative correlation between 'Recency' and 'Response' (-0.20). This implies that customers who made recent interactions are less likely to respond positively to campaigns.

3. Preprocessing¶

3.1. Encoding¶

In [30]:
education_dummies = pd.get_dummies(cleaned_df['Education'], prefix='Education')
marital_dummies = pd.get_dummies(cleaned_df['Marital_Status'], prefix='Marital_Status')
education_dummies.shape, marital_dummies.shape
Out[30]:
((2240, 4), (2240, 2))

3.2. Scale the Data¶

  • I will scale only numerical data and then concatenate them back with encoded education_dummies and marital_dummies
In [31]:
numerical_names = corr.columns.tolist()
numerical_data = cleaned_df[numerical_names].copy()

scaler = StandardScaler()
scaled_data = scaler.fit_transform(numerical_data)
scaled_data = pd.DataFrame(scaled_data, columns=numerical_names)

scaled_data = pd.concat([education_dummies, marital_dummies, scaled_data], axis=1)
scaled_data.head()
Out[31]:
Education_Basic Education_Graduation Education_Master Education_PhD Marital_Status_Relationship Marital_Status_Single Income Recency NumDealsPurchases NumWebVisitsMonth Response Accepted_Campaign Dependents Day_Enrolled Age Total_Cost Total_Purchases
0 0 1 0 0 0 1 0.24 0.31 0.35 0.69 2.39 -0.44 -1.26 1.53 0.99 1.68 1.31
1 0 1 0 0 0 1 -0.24 -0.38 -0.17 -0.13 -0.42 -0.44 1.40 -1.19 1.24 -0.96 -1.19
2 0 1 0 0 1 0 0.77 -0.80 -0.69 -0.54 -0.42 -0.44 -1.26 -0.21 0.32 0.28 1.04
3 0 1 0 0 1 0 -1.02 -0.80 -0.17 0.28 -0.42 -0.44 0.07 -1.06 -1.27 -0.92 -0.91
4 0 0 0 1 1 0 0.24 1.55 1.38 -0.13 -0.42 -0.44 0.07 -0.95 -1.02 -0.31 0.20
In [32]:
scaled_data.shape
Out[32]:
(2240, 17)

4. Dimensionality Reduction¶

Dimensionality reduction is a crucial technique in data analysis and machine learning. It serves various purposes, including:

  1. Curse of Dimensionality: With an increase in the number of features, data volume grows exponentially. This can lead to storage, computational, and analysis challenges. High dimensions often result in sparse data, making it harder to extract meaningful insights.

  2. Improved Model Performance: Some machine learning algorithms struggle with high-dimensional data, leading to overfitting. Dimensionality reduction can alleviate this issue by reducing noise and improving model generalization.

  3. Visualization: Visualizing data in high dimensions is challenging. Dimensionality reduction techniques project data into lower-dimensional spaces (e.g., 2D or 3D), aiding visualization and understanding.

  4. Noise Reduction: High-dimensional data often contains noise or irrelevant features. Dimensionality reduction can filter out noise and retain essential information.

In our dataframe (cleaned_encoded_df), we currently have 17 features, which represent a 17-dimensional space. Managing and interpreting data in such a high-dimensional space can be complex and resource-intensive. To address this, Principal Component Analysis (PCA) will be applied to reduce dimensionality while retaining the most critical information.

4.1. Initialize PCA object¶

Let's start with n_components = 3

In [33]:
pca = PCA(n_components=3)
pca.fit(scaled_data)
Out[33]:
PCA(n_components=3)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
PCA(n_components=3)
In [34]:
pca_data = pca.transform(scaled_data)
pca_data
Out[34]:
array([[ 2.04439308,  2.31707746, -0.51823267],
       [-1.60621049, -0.93211446,  0.73430373],
       [ 1.45289161, -0.84019837,  0.22872329],
       ...,
       [ 1.46662271, -0.94851091, -0.43401638],
       [ 1.1963261 , -0.67325204,  1.04476017],
       [-0.95154074,  2.1165869 , -0.44134608]])

Let's plot the result on the scree plot:

In [35]:
per_var = np.round(pca.explained_variance_ratio_ * 100, decimals=1)
labels = ['PC' + str(x) for x in range(1, len(per_var) + 1)]

plt.figure(figsize=(6, 4))
sns.barplot(x=labels, y=per_var, palette='magma')
plt.ylabel('Percentage of Explained Variance (%)')
plt.xlabel('Principal Component')
plt.title('Scree Plot')
plt.tight_layout()
sns.despine()

for i, v in enumerate(per_var):
    plt.text(i, v + 1, str(v) + '%', color='black', ha='center', va='bottom')

Note: With the specified number of components (n=3). The variance explained by each of the first three principal components is as follows:

  • PC1: Explains approximately 28.6% of the total variance.
  • PC2: Explains approximately 13.5% of the total variance.
  • PC3: Explains approximately 11.3% of the total variance.

These percentages indicate the proportion of the original variance retained in each principal component. Since we're using the first three principal components, the PCA has captured a total of around 53.4% of the total variance in the data.

This reduction in dimensionality will help streamline your data representation while preserving a substantial portion of its variability.

In [36]:
pca_df = pd.DataFrame(pca_data, columns=labels)
pca_df

pc1_values = pca_df['PC1']
pc2_values = pca_df['PC2']
pc3_values = pca_df['PC3']

fig = px.scatter_3d(pca_df, x=pc1_values, y=pc2_values, z=pc3_values)
fig = px.scatter_3d(pca_df, x=pc1_values, y=pc2_values, z=pc3_values, title='3D Scatter Plot of PC1, PC2, and PC3')
fig.update_layout(scene=dict(xaxis_title='PC1 - {0}%'.format(per_var[0]),
                             yaxis_title='PC2 - {0}%'.format(per_var[1]),
                             zaxis_title='PC3 - {0}%'.format(per_var[2])))
fig.show();

4.2. Identify the Optimal Number of Components¶

When deciding the number of principal components to retain, we will encounter a trade-off between reducing dimensionality and preserving information. This trade-off is influenced by the threshold we set for the variance explained.

  • Higher Threshold (e.g., 95% Variance Explained): Choosing a higher threshold, like capturing 95% variance explained, emphasizes retaining as much information as possible. This leads to keeping more principal components, resulting in a higher-dimensional representation of the data. While it preserves a significant portion of the original variability, it might necessitate more components.

  • Lower Threshold (e.g., 50% Variance Explained): Opting for a lower threshold, such as 50% variance explained, prioritizes dimensionality reduction. In this case, some information is sacrificed to achieve a substantial reduction in dimensionality. Although this approach results in a lower-dimensional data representation, it might not capture all subtle variations in the original data.

To determine the optimal number of components, I will apply two criteria:¶

  • ##### Total Variance Criterion: I will consider retaining a number of components that collectively explain for 50 to 70% of total variance.

  • ##### Kaiser Criterion: I will also employ the Kaiser criterion, which involves keeping principal components with eigenvalues greater than 1.

These two criteria will help guide the selection of the most suitable number of components for dimensionality reduction while preserving meaningful information.

Criterion #1 - Total Variance Criterion: 50-70% Cumulative Explained Variance¶

In [37]:
def find_components_near_threshold(data, from_threshold, to_threshold=None):
    """
    Find the number of principal components that explain the specified variance range/value.

    Parameters:
        data (array-like): The scaled data.
        from_threshold (float): The lower threshold for variance explained.
        to_threshold (float): The upper threshold for variance explained (optional).

    Returns:
        str: A string indicating the number of components that explain the specified variance range/value.
    """
    pca = PCA(n_components='mle', random_state=42)
    pca.fit(data)
    
    cumulative_variance = 0
    n_from = None
    n_to = None
    
    # Return a n_component if to_threshold = None
    for n in range(1, len(pca.explained_variance_ratio_) + 1):
        cumulative_variance += pca.explained_variance_ratio_[n - 1]
        
        if cumulative_variance >= from_threshold:
            n_from = n
            break
            
    # Return a list of n_components if to_threshold != None
    if to_threshold is not None:
        for n in range(n_from + 1, len(pca.explained_variance_ratio_) + 1):
            cumulative_variance += pca.explained_variance_ratio_[n - 1]
            
            if cumulative_variance >= to_threshold:
                n_to = n
                break
    
    if n_from is None:
        return f"No components explained for at least {from_threshold*100}% variance"
    elif n_to is None:
        return f"Number of components explained for at least {from_threshold*100}% variance is {n_from}"
    else:
        return f"Numbers of components explained for {from_threshold*100}% to {to_threshold*100}% variance are {', '.join(map(str, range(n_from, n_to)))}"
In [38]:
result1 = find_components_near_threshold(scaled_data, 0.5)
result2 = find_components_near_threshold(scaled_data, 0.5, 0.7)
print(result1)
print()
print(result2)

Number of components explained for at least 50.0% variance is 3

Numbers of components explained for 50.0% to 70.0% variance are 3, 4, 5

I will create a table showing Explained Variance by using pca.explained_variance_ratio_ so we can observe more easily:

In [39]:
variance_df = pd.DataFrame(columns=['Explained Variance', 'Cumulative Explained Variance'])

for i in range(1, len(scaled_data.columns) + 1):
    pca = PCA(n_components=i)
    pca.fit(scaled_data)
    explained_variance = pca.explained_variance_ratio_.round(2)[i-1]
    cumulative_variance = pca.explained_variance_ratio_.sum().round(2)
    variance_df.loc[f'{i}'] = [explained_variance, cumulative_variance]

variance_df.index.name = 'n_components'
variance_df.T
Out[39]:
n_components 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
Explained Variance 0.29 0.13 0.11 0.09 0.08 0.07 0.05 0.04 0.03 0.03 0.03 0.02 0.02 0.01 0.00 0.00 0.00
Cumulative Explained Variance 0.29 0.42 0.53 0.62 0.70 0.77 0.81 0.85 0.89 0.92 0.94 0.97 0.99 1.00 1.00 1.00 1.00

Let's plot:

In [40]:
plt.figure(figsize=(6, 4))
sns.lineplot(data=variance_df, x=variance_df.index, y='Cumulative Explained Variance', marker='o', label='Cumulative Explained Variance')
plt.axhline(y=0.5, color='c', linestyle='--', label='0.5 Threshold')
plt.axhline(y=0.7, color='c', linestyle='--', label='0.7 Threshold')

plt.title('Explained Variance vs. Number of PCA Components')
plt.xlabel('Number of Components')
plt.ylabel('Cumulative Explained Variance')
plt.legend()
plt.tight_layout();

Note:

As the result in the function, we have n_components = [3, 4, 5] as Explained Variance is in 50-70% range.

Now let's move to the next criterion.

Criterion #2 - Kaiser Criterion: Eigenvalues > 1¶

In [41]:
variance_df['Eigenvalue'] = pca.explained_variance_
variance_df.T
Out[41]:
n_components 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
Explained Variance 0.29 0.13 0.11 0.09 0.08 0.07 0.05 0.04 0.03 0.03 0.03 0.02 0.02 0.01 0.00 0.00 0.00
Cumulative Explained Variance 0.29 0.42 0.53 0.62 0.70 0.77 0.81 0.85 0.89 0.92 0.94 0.97 0.99 1.00 1.00 1.00 1.00
Eigenvalue 3.46 1.63 1.37 1.07 0.91 0.83 0.55 0.49 0.41 0.36 0.33 0.29 0.22 0.14 0.03 0.00 0.00
In [42]:
plt.figure(figsize=(8, 4))
sns.lineplot(data=variance_df, x=variance_df.index, y='Eigenvalue', marker='o')
plt.xticks(variance_df.index)
plt.axhline(y=1, color='r', linestyle='--', label='Kaiser Criterion: Eigenvalue > 1')
plt.title('Scree Plot with Kaiser Criterion')
plt.legend();
In [43]:
def find_components_eigenvalue_criterion(data):
    """
    Find the number of principal components based on the Kaiser Criterion (Eigenvalues > 1).

    Parameters:
        data (array-like): The scaled data.

    Returns:
        str: A string indicating the number of components based on the Kaiser Criterion.
    """
    cov_matrix = np.cov(data, rowvar=False)
    eigenvalues = np.linalg.eigvals(cov_matrix)
    n_components = np.sum(eigenvalues > 1)

    return f"Number of components based on Kaiser Criterion (Eigenvalues > 1): {n_components}"
In [44]:
find_components_eigenvalue_criterion(scaled_data)
Out[44]:
'Number of components based on Kaiser Criterion (Eigenvalues > 1): 4'

4.3. Conclusion about n_components¶

Criterion 1: Explained Variance (50-70%)

When using the criterion of explained variance, we aim to find the number of principal components that collectively capture a substantial portion of the variability in the data. In this case, we are interested in components that explain between 50% and 70% of the total variance in the dataset. Using the function find_components_near_threshold, we determine that for this range, the optimal number of components is 3, 4, and 5. These components strike a balance between dimensionality reduction and information retention.

Criterion 2: Kaiser Criterion (Eigenvalues > 1)

The Kaiser Criterion involves examining the eigenvalues associated with the principal components. Eigenvalues indicate the amount of variance explained by each principal component. A common guideline is to retain only those components with eigenvalues greater than 1. When applying this criterion using the function find_components_eigenvalue_criterion, we identify that the first 4 components have eigenvalues exceeding 1. These components contribute significantly to the variability in the data.

n_components = 4 to not only aligns with the Kaiser Criterion by retaining components with eigenvalues greater than 1, but it also satisfies the Explained Variance criterion by capturing a substantial portion of the variability in the data.

In [45]:
pca = PCA(n_components=4)
pca.fit(scaled_data)
pca_final = pd.DataFrame(
    pca.transform(scaled_data), 
    columns = (
        [
            "PC1", 
            "PC2", 
            "PC3", 
            "PC4",      
        ]
    )
)
pca_final.head()
Out[45]:
PC1 PC2 PC3 PC4
0 2.04 2.32 -0.52 -0.56
1 -1.61 -0.93 0.73 1.70
2 1.45 -0.84 0.23 0.16
3 -1.70 -1.10 -0.98 0.21
4 -0.43 -0.18 1.09 -0.96

Note: By reducing the dimensionality from 17 features to 5 principal components, we've effectively simplified the dataset while retaining the most critical information. This streamlined dataset is now more suitable for further analysis and modeling while avoiding the complexities associated with high-dimensional data.

5. Customer Segmentation using K-Means Clustering¶

In the next step of the analysis, I'll focus on customer segmentation using the K-Means clustering algorithm. To determine the optimal number of clusters (k) for our K-Means algorithm, I will employ the Elbow Method and the Silhouette Score to choose the optimal number of clusters.

Elbow Method:

  • Visual approach to determine optimal cluster count
  • Run K-Means for different k values
  • Plot WCSS against the number of clusters
  • Identify "elbow point" where WCSS reduction slows
  • Indicates balance between minimizing variance and overfitting

Silhouette Score:

  • Measures object similarity to its cluster and others
  • Score range: -1 to 1
  • Higher score means object matches its cluster well
  • Helps evaluate cluster quality and select best k value

5.1. Initialize model¶

In [46]:
ranges=range(1,11)
inertia = []
sil_scores = []
for n in ranges:
    kmeans = KMeans(n_clusters=n, 
                    n_init=10, 
                    algorithm ='lloyd',
                    random_state=42)
    kmeans.fit(pca_final)
    inertia.append(kmeans.inertia_)
    
    if n > 1:
        sil_score = silhouette_score(pca_final, kmeans.labels_)
        sil_scores.append(sil_score)
    else:
        sil_scores.append(0)

5.2. Choose k¶

In [47]:
plt.figure(figsize=(9, 4))
plt.subplot(1, 2, 1)
plt.plot(ranges, inertia, marker='o')
plt.xlabel('Number of Clusters')
plt.ylabel('Inertia')
plt.title('Elbow Method for Optimal k (Inertia)')

plt.subplot(1, 2, 2)
plt.plot(ranges, sil_scores, marker='o')
plt.xlabel('Number of Clusters')
plt.ylabel('Silhouette Score')
plt.title('Silhouette Score for Optimal k')
plt.tight_layout();

Another way to plot:

In [48]:
kmeans = KMeans(n_init=10, random_state=42)

elbow_visualizer = KElbowVisualizer(kmeans, k=(2,10))
elbow_visualizer.fit(pca_final)  
elbow_visualizer.show()
Out[48]:
<Axes: title={'center': 'Distortion Score Elbow for KMeans Clustering'}, xlabel='k', ylabel='distortion score'>
In [49]:
elbow_visualizer = KElbowVisualizer(kmeans, k=(2,10), metric='silhouette')
elbow_visualizer.fit(pca_final)  
elbow_visualizer.show();

5.3. Determining the Optimal Number of Clusters: Elbow vs. Silhouette¶

In our quest to determine the optimal number of clusters for customer segmentation, we employed two distinct methods:

  • Elbow Method: This method revealed a clear inflection point, or "elbow," at 4 clusters.

  • Silhouette Score: The Silhouette Score, which measures the quality of clustering, was highest for 2 clusters but also exhibited a substantial peak at 4 clusters.

Harmonization: To make an informed decision, I sought harmony between the insights gained from the Elbow Method and the Silhouette Score. Notably, we observed a convergence between the second peak at 4 clusters in the Silhouette Score and the elbow point at 4 clusters in the Elbow Method. This led us to the final decision of 4 clusters for our customer segmentation. This harmonious choice elegantly balances the reduction in inertia and the high quality of clustering.

In [50]:
kmeans = KMeans(n_clusters=4, 
                    n_init=10, 
                    random_state=42)
kmeans.fit(pca_final)
labels = kmeans.labels_
centroids = kmeans.cluster_centers_
pca_final['Cluster'] = labels
df['Cluster'] = labels
In [51]:
fig = plt.figure(figsize=(10,7))
ax = plt.subplot(projection='3d')
ax.scatter(xs = pca_final["PC1"], 
           ys = pca_final["PC2"], 
           zs = pca_final["PC3"], 
           s=50, 
           c= pca_final["Cluster"], 
           marker='o', 
           alpha = 0.5, 
           cmap = 'magma')
plt.title("Static 3D Clusters");
In [52]:
fig = px.scatter_3d(pca_final, x='PC1', y='PC2', z='PC3', color='Cluster', opacity=0.1, color_continuous_scale='magma')
fig.update_layout(title='Interactive 3D Clusters with Centroids')

fig.add_scatter3d(x=centroids[:, 0], y=centroids[:, 1], z=centroids[:, 2],
                  mode='markers', 
                  marker=dict(size=7, color='red', symbol='x'), 
                  showlegend=False)

6. Evaluate the Clusters¶

In [53]:
df.head(2)
Out[53]:
ID Year_Birth Education Marital_Status Income Kidhome Teenhome Dt_Customer Recency MntWines MntFruits MntMeatProducts MntFishProducts MntSweetProducts MntGoldProds NumDealsPurchases NumWebPurchases NumCatalogPurchases NumStorePurchases NumWebVisitsMonth AcceptedCmp3 AcceptedCmp4 AcceptedCmp5 AcceptedCmp1 AcceptedCmp2 Complain Z_CostContact Z_Revenue Response Year_Enrolled Accepted_Campaign Dependents Day_Enrolled Age Total_Cost Total_Purchases Cluster
0 5524 1957 Graduation Single 58138.00 0 0 04-09-2012 58 635 88 546 172 88 88 3 8 10 4 7 0 0 0 0 0 0 3 11 1 2012-09-04 0 0 663 57 1617 22 2
1 2174 1954 Graduation Single 46344.00 1 1 08-03-2014 38 11 1 6 2 1 6 2 1 1 2 5 0 0 0 0 0 0 3 11 0 2014-03-08 0 2 113 60 27 4 1
In [54]:
sns.countplot(x=df['Cluster'], palette='magma');
In [55]:
pd.pivot_table(data=df, index='Cluster', values = corr.columns.to_list(), margins=True).T
Out[55]:
Cluster 0 1 2 3 All
Accepted_Campaign 0.19 0.08 1.56 0.28 0.30
Age 49.13 41.84 43.75 48.12 45.19
Day_Enrolled 503.12 305.19 415.62 297.79 353.58
Dependents 1.43 1.19 0.19 0.48 0.95
Income 52187.86 34457.78 76702.93 71942.84 52237.98
NumDealsPurchases 4.98 1.84 1.28 1.49 2.33
NumWebVisitsMonth 6.80 6.40 3.73 3.05 5.32
Recency 51.22 47.66 37.02 54.10 49.11
Response 0.16 0.07 0.87 0.01 0.15
Total_Cost 636.41 98.13 1508.51 1072.78 605.80
Total_Purchases 15.39 5.88 19.52 18.55 12.54

Education and Marital_Status between Clusters¶

In [56]:
plt.figure(figsize=(12,4))
plt.subplot(1,2,1)
sns.countplot(data=df, x= 'Education', hue='Cluster', palette='magma');
plt.subplot(1,2,2)
sns.countplot(data=df, x= 'Marital_Status', hue='Cluster', palette='magma')
plt.tight_layout();

Note:

Education and Marital_Status appear to vary both within and among the customer segments, which means that these variables alone may not be the primary drivers of segment differentiation. In the context of the clustering analysis performed, it appears that other factors such as age, income, purchase behavior, and response to marketing campaigns have played a more significant role in distinguishing the segments.

7. Final conclusion¶

Segment 0: Active and High-Spending Shoppers

  • Segment Size: This segment is the largest, with 967 customers.
  • Demographics: They are relatively younger customers with a moderate income and 1 to 2 dependents.
  • Purchase Behavior: They are active purchasers, engaging in deals and making an average of 1.84 deals and 6.40 web visits per month. They have a moderate recency, indicating that they make purchases relatively frequently.
  • Spending Habits: They also have a higher Total_Cost, suggesting significant spending on various products in the last 2 years.
  • Campaign Response: However, they have a low response rate to marketing campaigns (0.08), suggesting that traditional campaign strategies may not be very effective for them.
  • Recommendation: Target this segment with exclusive deals and discounts, tailored to their high engagement and spending habits. It's essential to understand their preferences and what motivates them to respond to campaigns.

Segment 1: Value-Conscious Savers

  • Segment Size: This segment has 608 customers.
  • Demographics: They are relatively older customers with a high income and around 1 dependent.
  • Purchase Behavior: They engage in deals moderately, with an average of 1.49 deals, and have a lower number of web visits (3.05) compared to Segment 0. They have a relatively high recency, indicating recent purchases.
  • Spending Habits: They also have a significant Total_Cost, suggesting careful spending on various products in the last 2 years.
  • Campaign Response: Surprisingly, despite their lower engagement, they have a moderate response rate to marketing campaigns (0.28).
  • Recommendation: Build strong customer relationships and deliver personalized services to cultivate loyalty within this segment. They seem to respond well to campaigns, so tailoring campaigns to their preferences and values could be effective.

Segment 2: Affluent Explorers

  • Segment Size: This segment has 214 customers, the lowest among segments.
  • Demographics: They comprise relatively younger customers with very high income and minimal dependents.
  • Purchase Behavior: They engage in deals moderately and have moderate web visits.
  • Spending Habits: They also have a notable Total_Cost, indicating substantial spending on various products in the last 2 years.
  • Campaign Response: Despite having a lower recency, they have a very high response rate to marketing campaigns (1.56).
  • Recommendation: Craft marketing strategies to introduce novel offerings, emphasizing educational content to resonate with their curiosity. Despite their lower engagement frequency, their high response rate suggests a receptiveness to well-targeted campaigns.

Segment 3: Established High-Income Shoppers

  • Segment Size: This segment has 451 customers.
  • Demographics: They comprise relatively older customers with a moderate income and 1 to 2 dependents.
  • Purchase Behavior: They are moderate purchasers, engaging in deals and making an average of 4.98 deals and 6.80 web visits per month.
  • Spending Habits: They also have a notable Total_Cost, indicating significant spending on various products in the last 2 years.
  • Campaign Response: They have a moderate response rate to marketing campaigns (0.16).
  • Recommendation: Strategically target cross-selling and upselling campaigns to leverage their potential for elevated spending could be beneficial. These customers are active and engaged but may require specific offers to prompt them to respond to campaigns.